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A data-driven optimization of large-scale dry port locations using the hybrid approach of data mining and complex network theory

A data-driven optimization of large-scale dry port locations using the hybrid approach of data mining and complex network theory

Nguyen, Truong Van, Zhang, Jie, Zhou, Li ORCID: 0000-0001-7132-5935, Meng, Meng ORCID: 0000-0001-7240-6454 and He, Yong (2019) A data-driven optimization of large-scale dry port locations using the hybrid approach of data mining and complex network theory. Transportation Research Part E: Logistics and Transportation Review. ISSN 1366-5545 (In Press) (doi:https://doi.org/10.1016/j.tre.2019.11.010)

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Abstract

The paper proposes a two-stage approach that combines data mining and complex network theory to optimize the locations and service areas of dry ports in a large-scale inland transportation system. In the first stage, candidate locations of dry ports are weighted based on their eigenvector centrality in the complex network of association rules mined from a large amount of international transaction data. In the second phrase, dry port locations and their service areas are optimized using the gravity-based community structure. The method is validated in a real case study which optimizes a large-scale dry port network in Mainland China in the context of the Belt and Road Initiatives (BRI). As a result, optimal dry port locations include key transportation hubs that closely reflect the real BRI development plan, hence, the proposed approach is validated.

Item Type: Article
Uncontrolled Keywords: transportation, data mining, large scale optimization, dry ports, complex network theory
Subjects: H Social Sciences > H Social Sciences (General)
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Networks and Urban Systems Centre (NUSC)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group
Last Modified: 28 Nov 2019 09:13
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/26136

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